GG3206 Quantitative Methods for Social Scientists

Academic year

2025 to 2026 Semester 1

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

10

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 9

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Planned timetable

Tuesday 12-1 pm and Tuesday 2-4 pm

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr M Abed Al Ahad

Dr M Abed Al Ahad
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Mary Albed Al Ahad

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module is an introduction to quantitative data and survey analysis in the social sciences. Human geography research often involves collecting data from people, commonly in the forms of surveys or census/administrative records, and this module teaches you how to access, manage, explore, and analyse these data. We introduce core statistical concepts of likelihood, inference, hypothesis testing and regression modelling as well as descriptive analysis and data visualisation. The course uses the software R Studio, and you will be supported to write your own code. We also introduce you to a range of freely available secondary data on contemporary human geography topics. Teaching is delivered through a combination of lectures on theoretical concepts, and in-person R practicals in a computer lab. This module will equip you with data literacy skills that will be useful for careers in academia, industry, or government. The module combines well with GG3208: Survey Design.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS SD2100

Assessment pattern

Coursework = 100%

Re-assessment

Coursework = 100%

Learning and teaching methods and delivery

Weekly contact

1 lecture (x 7 weeks) 2 practicals (x 10 weeks)

Scheduled learning hours

27

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

75

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Students will learn how to calculate basic descriptive statistics and conduct hypothesis tests
  • Students will learn the principles of a range of statistical techniques commonly employed in quantitative social science research and quantitative human geography, including multiple linear regression, and spatial regression
  • Through the lab practicals, students will gain experience applying regression techniques, using statistical software (R) in order to get hands-on experience working with real data on a range of topics
  • Students will understand the practical considerations when designing a questionnaire
  • Students will learn how to access and explore large scale secondary datasets containing social data